The purpose of this paper is to describe and analyse a mechanical device designed to enhance the safety of a motor vehicle. The topic is addressed by analysing the method of absorbing kinetic energy during a car collision with an obstacle. The article analyses opportunities to convert motor vehicle’s kinetic energy into another type of energy in the case of collision. For this purpose, various mechanical, hydraulic or pneumatic devices are normally used. Such devices are designed to absorb collision energy and reduce or eliminate its impact on the driver, the passengers or cargo in the motor vehicle. The absorber may be used as an additional element of safety to the passenger and the cargo. The energy absorber described in the present article incorporates hydraulic, pneumatic and mechanical components. The description of the absorber presented here is based on mathematical calculations characterizing mechanical, pneumatic and hydraulic processes in the equipment. The analysis of the developed mechanism employs a special application to calculate major parameters of the motor vehicle and the installed absorber. The article also gives a sensitivity analysis of the effect of the rod length on the decrement of the vehicle's kinetic energy.
Aesthetic patterns are widely used nowadays, e.g., in jewellery design, carpet design, as textures and patterns on wallpapers, etc. Most of the work during the design stage is carried out by a designer manually. Therefore, it is highly useful to develop methods for aesthetic pattern generation. In this paper, we present methods for generating aesthetic patterns using the dynamics of a discrete dynamical system. The presented methods are based on the use of various iteration processes from fixed point theory (Mann, S, Noor, etc.) and the application of an affine combination of these iterations. Moreover, we propose new convergence tests that enrich the obtained patterns. The proposed methods generate patterns in a procedural way and can be easily implemented on the GPU. The presented examples show that using the proposed methods we are able to obtain a variety of interesting patterns. Moreover, the numerical examples show that the use of the GPU implementation with shaders allows the generation of patterns in real time and the speed-up (compared with a CPU implementation) ranges from about 1000 to 2500 times.
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